Digital images captured by digital cameras are corrupted by “noise,” wherein noise can be defined as unwanted random variations in the digital image. Noise can arise from a number of sources such as sensor shot noise and fixed-pattern noise. The amount of noise in a digital image can depend on many factors such as sensor design, exposure level, and digital image processing applied to the image. Denoising algorithms are often used to “denoise” captured digital images (i.e., reduce the level of noise) in order to improve the signal-to-noise (SNR) ratio of the captured digital images. Noise in digital camera images generally depends on the signal level (i.e., the image pixel values); this type of noise is commonly referred to as “signal-dependent noise.” Traditional denoising algorithms assume the level of noise to be independent of the image pixel values. As a result, such algorithms are inadequate to deal with signal-dependent noise.
One method described by Rudin et al., in the article “Nonlinear total variation based noise removal algorithms” (Physica D, Vol. 60, pp. 259-268, 1992) uses total variation minimization to generate denoised image.
Another method taught by Foi et al., in the article “Pointwise shape-adaptive DCT for high-quality denoising and deblocking of grayscale and color images” (IEEE Transactions on Image Processing, Vol. 16, pp. 1395-1411, May 2007) analyzes noisy image using a shape-adaptive discrete cosine transform (DCT).
Coifman et al., in the article “Translation-invariant de-noising” (Lecture Notes in Statistics: Wavelets and Statistics, Springer Verlag, New York, pp. 125-150, 1995) use translation invariant thresholding of the wavelet coefficients of the noisy image to produce a denoised image.
All of the above denoising algorithms assume noise to be independent of the image pixel values and are therefore inadequate to deal with signal-dependent noise.
Hirakawa et al., in the article “Image denoising for signal-dependent noise” (IEEE International Conference on Acoustics, Speech, and Signal Processing, Vol. 2, pp. 29-32, 2005) teaches a rather elegant signal-dependent denoising technique. In this approach, the total least square (TLS) approach is used for modeling the uncertainties in the noisy image and to reduce the signal-dependent noise. However, this approach is computationally demanding and too slow for many applications.
Thus, there exists a need for an efficient signal-dependent denoising algorithm that preserves salient features of the image.